Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
[HTML][HTML] Review of the emotional feature extraction and classification using EEG signals
J Wang, M Wang - Cognitive robotics, 2021 - Elsevier
As a subjectively psychological and physiological response to external stimuli, emotion is
ubiquitous in our daily life. With the continuous development of the artificial intelligence and …
ubiquitous in our daily life. With the continuous development of the artificial intelligence and …
Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network
H Li, M Ding, R Zhang, C Xiu - Biomedical signal processing and control, 2022 - Elsevier
Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer
interaction. Traditional neural networks often use serial structure to extract spatial features …
interaction. Traditional neural networks often use serial structure to extract spatial features …
Automated feature extraction on AsMap for emotion classification using EEG
Emotion recognition using EEG has been widely studied to address the challenges
associated with affective computing. Using manual feature extraction methods on EEG …
associated with affective computing. Using manual feature extraction methods on EEG …
[HTML][HTML] COVID-19 detection using chest X-ray images based on a developed deep neural network
Aim Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at
21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities …
21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities …
Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network
EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
[HTML][HTML] Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks
J Wang, Z Wang, G Liu - Alexandria Engineering Journal, 2024 - Elsevier
Electroencephalography (EEG) signals are crucial for investigating brain function and
cognitive processes. This study aims to address the challenges of efficiently recording and …
cognitive processes. This study aims to address the challenges of efficiently recording and …
Holistic approaches to music genre classification using efficient transfer and deep learning techniques
SK Prabhakar, SW Lee - Expert Systems with Applications, 2023 - Elsevier
With the rapid development of high-tech multimedia technologies, many musical resource
assets are available online and it has always triggered an interest in the classification of …
assets are available online and it has always triggered an interest in the classification of …
Deep learning for detecting multi-level driver fatigue using physiological signals: A comprehensive approach
A large share of traffic accidents is related to driver fatigue. In recent years, many studies
have been organized in order to diagnose and warn drivers. In this research, a new …
have been organized in order to diagnose and warn drivers. In this research, a new …
Developing a deep neural network for driver fatigue detection using EEG signals based on compressed sensing
In recent years, driver fatigue has become one of the main causes of road accidents. As a
result, fatigue detection systems have been developed to warn drivers, and, among the …
result, fatigue detection systems have been developed to warn drivers, and, among the …